Forest Growth and Tree Structure Detection Based on Remote Sensing

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3404

Special Issue Editors


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Guest Editor
Laboratory of Forest Biometrics, Department of Forestry & Natural Environment, School of Geotechnical Sciences, International Hellenic University, 66100 Drama, Greece
Interests: forest growth; tree and stand structure modelling; forest management; mixed-effects models; remote sensing
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Guest Editor
Laboratory of Forest Resource Management and Bioeconomics, Department of Forestry & Natural Environment Sciences, Faculty of Geotechnical Sciences, International Hellenic University, 66100 Drama, Greece
Interests: multi-criteria sustainable forest management; decision support systems; AI predictive modeling for sustainable forest management

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Guest Editor
Department of Forestry and Natural Environment, International Hellenic University, 1st Km Drama-Mikrochori, GR 66100 Drama, Greece
Interests: forest ecology; landscape ecology; biodiversity conservation; restoration ecology; fire ecology; urban landscapes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable forest management requires complex and wide ranging information particularly related to forest inventory quantitative assessments and monitoring. Such information should characterize forest growth attributes at tree and stand level, forest structure and forest composition, and it should be accurate, up to date and spatially referenced. Remote Sensing technology, such as airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), high spatial resolution (HSR) and very high spatial resolution (VHSR) satellite optical imagery is gradually replacing traditional inventory approaches in terms of data acquisition both through direct measurements or indirect through modeling approaches. Several challenges are being faced by researchers and analysts related to accuracy, scale, cost, technical capacity and data processing methods for sustainable forest management decision making at all levels; operational, tactical and strategic.

This Special Issue calls for high quality updated research papers focused on the use of remote sensing technology for multi-scale data collection, data processing and predictive modeling approaches on forest growth and yield attributes, tree structure detection, forest structure and forest composition related to all kind of forest species.

Dr. Dimitrios I. Raptis
Prof. Dr. Vassiliki Kazana
Dr. Panteleimon Xofis
Guest Editors

Manuscript Submission Information

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Keywords

  • airborne laser scanning
  • terrestrial laser scanning
  • digital aerial photogrammetry
  • satellite optical imagery
  • predictive modeling
  • forest growth and yield attributes
  • forest structure and composition
  • machine learning
  • point clouds

Published Papers (2 papers)

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Research

17 pages, 6199 KiB  
Article
Land Cover Changes in Evrytania Prefecture (Greece)
by Spyridon Kaloudis, Maria Glykou, Stavroula Galanopoulou, Georgios Fotiadis, Constantine Yialouris and Dimitrios Raptis
Forests 2023, 14(7), 1462; https://doi.org/10.3390/f14071462 - 17 Jul 2023
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Abstract
To record land cover changes over time, geographic information systems software was used for selecting and studying sampling surfaces in ortho-aerial photographs. In particular, ortho-aerial photographs of the years 1945 and 2015 were used to record changes in land cover. A total of [...] Read more.
To record land cover changes over time, geographic information systems software was used for selecting and studying sampling surfaces in ortho-aerial photographs. In particular, ortho-aerial photographs of the years 1945 and 2015 were used to record changes in land cover. A total of 103 test surfaces were obtained, which consisted of 25 cells each. The results showed that the area and density of forest cover have increased significantly during the study period. Changes in land cover, and in particular forest cover, are mainly attributed to (a) the gradual decline of the population, and therefore to the decline in man-made interventions such as crops, nomadic herd grazing, and logging, and to (b) natural species competition. Moreover, the effect of climatic change and the reduction in human presence on fir treelines was examined. Based on the results, no clear evidence about treeline changes was found. Also, the effect of soil and topographic factors on land cover changes, as well as the prediction capability of land cover changes, were examined using an artificial neural network. Promising results came out that could provide substantial explanations for land cover changes and quantify the effect of environmental factors on vegetation evolution. Full article
(This article belongs to the Special Issue Forest Growth and Tree Structure Detection Based on Remote Sensing)
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18 pages, 15564 KiB  
Article
Use of GIS in Selecting Suitable Tree Crop Cultivation Sites in Mountainous Less Favoured Areas: An Example from Greece
by Stefanos Tsiaras and Christos Domakinis
Forests 2023, 14(6), 1210; https://doi.org/10.3390/f14061210 - 11 Jun 2023
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Abstract
The aim of this paper is the selection of suitable tree crop cultivation sites in mountainous less favoured areas, as a forest policy measure under the scope of sustainable development. Ten different crop types were proposed as being most suitable in the study [...] Read more.
The aim of this paper is the selection of suitable tree crop cultivation sites in mountainous less favoured areas, as a forest policy measure under the scope of sustainable development. Ten different crop types were proposed as being most suitable in the study area, Pierion Municipal Unit, which is located in the Municipality of Katerini, in the Pieria Prefecture of Greece. In order to determine the most suitable sites for cultivation, data layers that involved the factors of topography, climate, pedology and geology were derived from existing maps and free-of-charge datasets, so that they could be consequently processed with the aid of Geographic Information Systems (GIS). The data processing was performed by following criteria, which were established in accordance with the current literature and were translated into Boolean algebra expressions. The latter helped to identify locations where the values of the factors that were employed were most favourable for the cultivation of walnut trees (Juglans sp.), olive trees (Olea sp.), cherry trees (Prunus sp.), apple (Malus sp.), dogwood trees (Cornus sp.), pomegranate trees (Punica sp.), chestnut trees (Castanea sp.) and other crop types. Moreover, the resulting map indicated that the majority of the suitable sites for cultivation were considered favourable for growing walnut trees (24.9%), followed by cherry trees (19.6%) and olive trees (12.1%). Proposing the most suitable cultivations within the study area contributes to forest policy planning and promotes the sustainable development of mountainous less favoured areas, leading to a more rational management of natural resources, a raised awareness of environmental protection, the maintenance of the local population and income enhancement through the production of high quality crops and sustainable yields. Full article
(This article belongs to the Special Issue Forest Growth and Tree Structure Detection Based on Remote Sensing)
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